基于模糊Petri网和Genetic-PSO算法的动态不确定性知识表示方法

摘要

针对复杂系统中不确定性信息的演变特性，提出对其动态适应的基于模糊 Petri 网和遗传-粒子群( GPSO)算法的不确定性知识表示方法。在基于模糊Petri网的不确定性知识表示模型的基础上，对该模型进行精确数学表示，并采用 GPSO实现对不确定性表征参数的动态求解和自学习。最后通过在运载火箭伺服机构故障诊断上的应用验证基于GPSO的自学习模糊Petri网的有效性。%To represent and reason uncertain knowledge in the complex system dynamically and effectively, a self-adaptive method based on fuzzy-Petri net ( FPN ) and genetic particle swarm optimization ( GPSO ) algorithm is proposed. In this method, the knowledge-representation model based on FPN is established to build the mathematical model. And the GPSO is used in self-learning of the uncertain parameters to achieve self-adaptation of the model. Finally, a servo mechanism fault diagnose of launch vehicle is used to verify the proposed method.